1. Field of the Invention
The present invention belongs to a technical field of active suppression engineering. For instance, it belongs to a technical field of active damping when a cyclic signal is a vibration, and to a field of active noise suppression when a cyclic signal is a noise. Thus, depending on the types of the cyclic signal, its application field can be expanded widely.
2. Description of the Related Art
Japanese Unexamined Patent Publication (KOKAI) No. 8-44,377 is derived from Japanese Patent Application No. 6-201,384, and discloses a DXHS-LMS algorithm. Compared to a previous FX-LMS algorithm, the DXHS-LMS algorithm produces an advantage in that, although the calculation steps are reduced, the convergence speed can be improved. The FX-LMS algorithm is referred to in the publication and Japanese Unexamined Patent Publication (KOKAI) No. 8-272,378.
However, even the DXHS algorithm cannot necessarily exhibit an appropriate following characteristic when a transfer function of a controlled system is a resonance system whose gain shows a sharp peak. For example, when angular frequencies .omega..sub.k to be suppressed varies rapidly in a cyclic signal f(n), the adaptation, effected by the adaptive control system according to the DXHS algorithm, cannot fully follow the rapid variation. As a result, an error signal e (n) may sometimes enlarge to a non-negligible extent.
There is a data tabulation method, one of the countermeasures for coping with the rapid variation of the specific components of the angular frequencies .omega..sub.k to be suppressed in the cyclic signal f(n). In the data tabulation method, the amplitudes and phases of the adaptive signal y(n) are converted into a tabulated data for each range of the angular frequencies .omega..sub.k, and the amplitudes and phases of the adaptive signal y(n) are read out from the tabulated data to renew the components of the adaptive coefficient vector W(n) when the angular frequencies .omega..sub.k have shifted. Thus, the convergence speed can be improved.
Whilst, in the method employing the tabulated data, the amplitudes and phases of the adaptive signal y(n) vary discontinuously upon reading out the data from the tabulated data in accordance with the variation of the angular frequency .omega..sub.k. Accordingly, there arises inconvenience in that the user feels uneasiness. In addition, the method requires a memory capacity for storing the tabulated data. Consequently, there also arises other inconvenience in terms of the memory capacity. Thus, although the method employing the tabulated data can compensate for the lack of the following characteristic in the DXHS algorithm, it results in the new inconveniences. Therefore, the method cannot be smart and reasonable measures for solving the problem associated with the DXHS algorithm.
Hence, the inventor of the present invention returned to the square one. He then studied into the cause of the delayed adaptation which results in the lack of the convergence speed in the DXHS algorithm when the controlled system is the resonance system. As a result, he thought of the following facts: the maximum value of the renewing coefficient (or step size parameter) is designed so that it is stable in the high-gain frequency where it is highly probable to diverge; and accordingly the renewing coefficient is extremely small in the other frequency regions. In other words, the renewing coefficient is designed so that the adaptive control system does not diverge even in the high-gain frequency range. Hence, the renewing coefficient cannot be set at such a sufficiently large value that the convergence speed is sufficiently fast in the other frequency regions.